›› 2014, Vol. 34 ›› Issue (5): 87-93.doi: 10.3780/j.issn.1000-758X.2014.05.012

• 技术交流 • 上一篇    

应用改进果蝇优化算法的月面巡视器路径规划

 毛正阳1, 方群1, 李克行2, 张传鑫3   

  1. (1西北工业大学航天学院航天飞行动力学技术重点实验室,西安710072)
    (2空间智能控制技术国家重点实验室,北京100190)
    (3上海微小卫星工程中心,上海201203)
  • 收稿日期:2013-12-23 修回日期:2014-03-07 出版日期:2014-10-25 发布日期:2014-10-25
  • 作者简介:毛正阳 1987年生,2010年毕业于太原理工大学机械设计及其自动化专业,现为西北工业大学航天飞行动力学技术重点实验室博士研究生。研究方向为飞行器设计、路径规划。
  • 基金资助:

    国家自然科学基金(61004124),智能控制重点实验室开放基金(9140C590206120C59222)资助项目

PathPlanningforLunarRoverBasedonModifiedFruitFlyOptimizationAlgorithm

 MAO  Zheng-Yang1, FANG  Qun1, LI  Ke-Xing2, ZHANG  Chuan-Xin3   

  1. (1NationalKeyLaboratoryofAerospaceFlightDynamics,SchoolofAstronautics,NorthwesternPolytechnicalUniversity,Xi′an710072)
    (2NationalLaboratoryofSpaceIntelligentControl,Beijing100190)
    (3ShanghaiEngineeringCenterforMicrosatellites,Shanghai201203)
  • Received:2013-12-23 Revised:2014-03-07 Online:2014-10-25 Published:2014-10-25

摘要: 文章针对果蝇优化算法易陷入局部最优的问题,对果蝇算法中的味道浓度判定值进行改进,并将其用于月球探测巡视器的动态路径规划。为验证算法的有效性,将改进果蝇优化算法与粒子群优化算法的路径规划寻优特性进行了仿真对比分析,结果表明改进果蝇优化算法具有良好的实时性,并有效解决了算法易陷入局部最优的问题。考虑到月球探测巡视器在沿规划路径进行月面巡视的过程中,有可能遇到未知障碍物的情况,提出了动态环境下月球巡视器遇到未知静态障碍物的避障策略。

关键词: 粒子群算法, 局部最优, 果蝇优化算法, 动态路径规划, 月球探测

Abstract: Given that fruit fly optimization algorithm was restricted for falling into local optima easily, the smell concentration judge value S in fruit fly optimization algorithm was modified for dynamic path planning on lunar surface. The optimizing performance for path planning between the fruit fly optimization algorithm and the particle swarm optimization algorithm was compared through the simulations. Results show that the path planning based on the modified fruit fly optimization algorithm has better instantaneity and can not fall into local optima easily. Finally, an avoidance strategy was proposed for avoiding the unknown static obstacles that the lunar rover encounters in a dynamic environment.

Key words: Particleswarmoptimizationalgorithm, Localoptima, Fruitflyoptimizationalgorithm, Dynamicpathplanning, Lunarexploration